SCORE Mali

SCORE is a robust scientific assessment tool that is carefully calibrated to each context to investigate societal dynamics and guide evidence-based policy and programme design for enhancing social cohesion. For a more detailed explanation of SCORE methodology, please read the short methodology paper here.

The SCORE in Mali was implemented by SeeD in collaboration with Search for Common Ground Mali (SFCG) as part of the «My Education, My Future» project, with financial support of UNICEF.

The low level of schooling rates in Mali are the result of several interdependent factors. Different kinds of insecurity, irregular school functioning, the under-appreciated value of education in the community, and financial difficulties in many households all contribute to poor schooling outcomes.

The approach taken for this study is based on two methodologies which have been used in an integrated way to produce a multi-level analysis. This includes a qualitative methodology developed by Search for Common Ground, called Conflict Scan, and a quantitative aspect based on the SCORE index, developed by SeeD.

The data collection took place in May and June 2021 in three selected regions (Gao, Mopti and Timbuktu), with a total sample of 1424 (202 teachers, 611 parents and 611 adolescents). Sampling was calibrated according to the geographical distribution within each of the Cercles.

Table 1. Cercles sampled in SCORE Mali and the corresponding sample size.

Cercle	Parents	Adolescents	Teachers	Total
Mopti	154	154	51	359
Bandiagara	130	130	43	303
Gao	100	100	33	233
Ansongo	55	55	18	128
Timbuktu	53	53	18	124
Gourma Rharous	46	46	15	107
Niafunké	73	73	24	170
Total	611	611	202	1424

 

The key research questions of the SCORE in Mali are:

• What are the conflicts in the studied regions?
• What are the resilience factors that allow some schools to operate despite the persistence of conflict dynamics in their locality?
• What are the resilience factors that encourage parents to send their children to school despite various forms of insecurity?
• What are the determinants that tend to make school life appreciable by children and teachers ?

 

The indicators of SCORE Mali, as well as the relationships between them can be explored and disaggregated interactively on this platform. For information on how to use the platform, you can watch the short video on our Facebook page here or read the How to Read SCORE manual here. 

SCORE Process

Our evidence-based peacebuilding methodology combines an extensive participatory research process with advanced data analysis to identify the drivers of conflict dynamics and peaceful social change. It draws inspiration from multiple disciplines such as sociology, psychology, international relations and security studies and is flexible enough to incorporate new research findings, global policy guidelines and the realities of each local and regional context. The methodology is used to recommend peacebuilding solutions related to social cohesion and reconciliation, youth inclusion, gender empowerment, governance and anti-corruption and urban cohesion. The collection and interpretation of data in these areas allows us to provide policy recommendations, which predict how peacebuilding objectives might be achieved through the implementation of specific policies and projects. We invest in ongoing learning and innovation to improve our methodology so we can provide more inclusive and impactful policy and programme advice to governments and international organisations. 

The first editions of our methodology were developed in 2012 in Cyprus in a partnership between UNDP and USAID. Our approach is based on participatory research and mixed methods, in which multi-level stakeholder consultations, focus groups and interviews inform the design and calibration of the questionnaire, which draws from our extensive library of measurement instruments and indicators. Our methodology is underpinned by a Content Framework, which helps us align research objectives with the specific policy outcomes of different partners; a Process Framework, which adopts participatory research principles and ensures local ownership of project results; and an Analytical Toolkit, which combines different advanced statistical methods for scientifically robust investigation of SCORE data sets. For a brief summary of what each of these frameworks entail, please click here.
 

SCORE Vocabulary

Dimensions are thematic categories that organise the different indicators on the platform. You can investigate multiple indicators organised under six different dimensions.

Indicators measure a particular phenomenon (e.g. economic security, active citizenship, level of education, tolerance to corruption etc.), the definition of which can be found in the glossary search box. Indicators are generally measured using a minimum of 3 questionnaire items and their validity confirmed using statistical reliability tests to ensure that the different dynamics underlying the indicator are well-captured. A score from 0 to 10 is calculated for each indicator. 0 means the phenomenon the indicator is measuring is not observed in the context at all, and 10 means that the phenomenon is prevalent. For example, if we want to denote the extent to which people feel safe from violence in daily life, a score of 0 would mean that no one feels secure, while 10 would signify that every person feels secure.

Heatmaps show how indicators are represented across different geographical areas, illustrating regional differences to identify areas of concern or priority to tailor policies and programmes and to improve resource allocation more precisely.  

Path Analyses (Predictive Models) represent relationships between indicators based on advanced statistical analysis (e.g. regression, network analysis and structural equation modelling). In models, the relationships are directional, and they should be read from left to right. They have predictive power and are used to identify key drivers of change in society. Models reveal what influences an indicator or what this indicator influences itself. Indicators can be “drivers” as they positively or negatively predict the other indicators they are linked to. In a model, the indicator that the drivers are predicting is called an ‘outcome’. Outcomes are at the right end of the model, and they are usually the end goals that we want to influence. Red connecting lines in models represent a negative relationship and blue connecting lines represent a positive relationship between indicators. The thickness of arrows indicates the strength of the relationship between the indicators. Models should not be confused with correlations, where lines represent associations, but are not directional.